Fix "Similar Content in Our Collection" issue on Adobe Stock скачать в хорошем качестве

Fix "Similar Content in Our Collection" issue on Adobe Stock

🔥 STOP Adobe Stock Rejection! How to Fix the "Similar Content in Collection" Issue for Stock Contributors (AI Image Fix) 📩Contact me, WhatsApp: +8801779546977 Facebook:   / jubayer.ahmed.zubu   Email: [email protected] Are you an Adobe Stock contributor constantly battling the frustrating “Similar Content in our Collection” rejection issue? You're not alone! This detailed video addresses one of the most common challenges faced by AI content creators on the platform and reveals a proven strategy to significantly reduce your Adobe Stock rejection rate. The Problem: Why You Are Facing Similar Content Rejection The issue of similar content in collection has become increasingly prevalent, especially since Adobe Stock began accepting AI-generated images. Adobe Stock aims to maintain a collection where customers can easily find distinct and relevant content. Therefore, they reject content that is "too repetitive" or closely matches files already in the collection. The root cause often lies in the widespread use of Large Language Models (LLMs) like ChatGPT or Gemini to generate prompts. When numerous stock contributor tips utilize the same tools and request prompts for specific niches (like food or vector images), the LLMs generate similar prompt outputs. This results in contributors uploading visually similar images, triggering Adobe Stock’s cross-match rejection system. The Solution: Reverse Engineering to Beat Similar Content Rejection We introduce a successful reverse engineering formula designed to teach the AI what content is already oversaturated, helping you learn how to avoid rejection on Adobe Stock. Here is the strategy demonstrated in the video: 1. Gather the Evidence: Take a screenshot of the images that have been rejected specifically for the similar content issue. 2. Teach the AI: Upload the screenshot to your LLM (like ChatGPT). Since the LLM does not inherently know what images are already in the Adobe Stock collection, you must inform it that these images were rejected. 3. Analyze and Refine: Instruct the LLM to deeply analyze the rejected images to find common similarities, styles, or designs. 4. Generate Unique Prompts: Ask the LLM to provide refined and filtered prompts. These new prompts will generate images that remain in your chosen niche but are distinct and unique—perhaps by slightly tweaking the color, background, or design—thus preventing rejection. Results and Contributor Advice By implementing this reverse engineering technique, the contributor’s rejection rate significantly improved. The rate of accepted images jumped from a 50/50 ratio to approximately 80 accepted images for every 20 rejected (when uploading 100 images). Essential Stock Contributor Tips: • Niche Focus: Adobe recommends keeping your content within a specific niche or category (like food) as this increases the potential for sales over the long run. • Avoid Branding/Logos: Never upload AI-generated images featuring common company logos or branding (e.g., Apple's MacBook) without legal permission or release, as this puts your entire account at high risk of disablement. This video also briefly demonstrates how to use automation tools (like VS Code and a program called "Whis") to process the new, refined prompts, allowing you to generate hundreds of unique images efficiently